Detrended Cross-correlation Analysis Method Research Articles

Hedging effectiveness of Chinese Treasury bond futures: New evidence based on nonlinear analysis

Using multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DCCA) methods, this paper investigates the hedging effectiveness of Chinese Treasury bond futures. We utilize MF-DFA approach and show that Treasury bond spot and futures markets are multifractal. Moreover, results based on MF-DCCA suggest persistent cross-correlation between 10-year Treasury bond spot and futures, but no significant cross-correlation between 5-year Treasury bond spot and futures. Interestingly, there also exists persistent cross-correlation between 10-year Treasury bond futures and other Treasury bonds (5-year and long-term). Results based on MF-CCA verify the persistent cross-correlations above. Our findings imply that 10-year Treasury bond futures contracts can serve as better tools to hedge interest rate risk.

China's copper futures market efficiency analysis: Based on nonlinear Granger causality and multifractal methods

Abstract: Price discovery and market efficiency are the centerpiece of the market microstructure design. This study investigates the nonlinear correlation between the spot and futures prices in China's copper market using nonlinear Granger causality and multifractal methods, and it further analyzes the dynamic efficiency of China's copper futures market. According to the results of the nonlinear Granger causality test, there is a significant bidirectional nonlinear causality between the spot and futures prices in China's copper market. Excluding the effects of the first and second orders, there is still a high-order correlation between the spot and futures prices of the copper market. Additionally, the multifractal detrended cross-correlation analysis (MF-DCCA) method is used to obtain the long-term correlations of high order in the spot and futures markets. The time-varying rolling Hurst exponent indicates that the efficiency of China's copper futures market has gradually become more effective over time.

Asymmetric Multifractal Analysis of Rebar Futures and Spot Market in China

In this study, a wavelet denoising method is first used to eliminate the influence of noise. Then, an overlapping smooth window technique is introduced into the asymmetric multifractal detrended cross-correlation analysis method, which was combined with the multiscale multifractal analysis method, resulting in the proposed asymmetric multiscale multifractal detrended cross-correlation analysis method. This method not only remedies the pseudo-fluctuation defect of the traditional method, but also explores the asymmetric multifractal cross-correlation between China’s rebar futures and spot markets at different scales. The results show the existence of an asymmetric multifractal cross-correlation between rebar futures and spot markets with upward and downward trends at different scales. This cross-correlation is highly complex at the small-scale, and more pronounced when the futures market is in an uptrend.

MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS OF HIGH-FREQUENCY STOCK SERIES BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION

We used the multifractal detrended cross-correlation analysis (MFDCCA) method based on ensemble empirical mode decomposition (EEMD) to study the 5-min high-frequency data of two Chinese stocks and two US stocks. Using EEMD method to decompose the original high-frequency stock data can effectively reduce the interference of noise on the series, which helps to reveal the internal characteristics of the stock system and extract more accurate and rich information. We first conducted a cross-correlation test and cross-correlation coefficient analysis on the reconstructed stock data of two groups, and found that there is a cross-correlations between them. Then we used the EEMD-based MFDCCA method to analyze the cross-correlation between the data and found that there are significant cross-correlations between DJI and NASDAQ and between SSEC and SZSE. The cross-correlation of the two Chinese stocks is stronger than that of the two US stocks. The MFDCCA results of the comparison of the original series with the reconstructed series after decomposition by the EEMD method show that the reconstructed series can display more internal details of the multifractal cross-correlation metrics compared with the original series.

Fractal Analysis of Power Grid Faults and Cross Correlation for the Faults and Meteorological Factors

Power-grid faults pose a great threat to the economy and to social stability. In this paper, the detrended fluctuation analysis method is used to investigate the scaling properties of power grid faults and the correlation between faults and meteorological factors that are closely related to power grid faults. Multifractal detrended fluctuation analysis showed that the fault time series were multifractal. Further investigations revealed the origins of multifractality in power-grid fault time series, and the results showed that the temporal correlations in the data represent the distribution of the returns, which are a significant source of multifractal scaling. Then, cross correlations between the fault and the four meteorological factors were investigated using the detrended cross-correlation analysis method. The results showed that maximum wind speed has a considerable impact on the number of transmission system line faults per day, whereas daily precipitation, daily mean air temperature, and maximum wind speed have a considerable impact on the number of distribution system line faults per day.

China’s soybean crush spread: Nonlinear analysis based on MF-DCCA

In this paper, we investigate cross-correlations among the soybean, soymeal, and soyoil futures return series at China’s Dalian Commodity Exchange using both multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DCCA) methods. We find that there exists multifractality in the soybean, soymeal and soyoil futures return series using MF-DFA method. Moreover, results of MF-DCCA demonstrate that there exists a power-law cross-correlation and significant multifractal features among soybean, soymeal and soyoil futures returns. Furthermore, by comparing the multifractality of the original series to the shuffled and surrogated series, we find that both the long-range correlations and fat-tailed distribution contribute to the multifractality in cross-correlation among soybean, soymeal and soyoil futures return series in Dalian Commodity Exchanges. Furthermore, we use nonlinear Granger causality test and show that anyone of the three products returns is the Granger cause of the other two products returns.

Dynamical behavior of combined detrended cross-correlation analysis methods in random walks and Lévy flights

In this paper, the utility and the correlation technique of the detrended cross-correlation analysis and the combined detrended cross-correlation analysis methods are treated for the stochastic properties of the random walk and the Lévy flight via nucleotide sequence data. Data are extracted definitely from twelve genomic DNA sequences (HUMHBB 1-6, Amazonmolly, Bushbaby, Cow, Dolphin, Elephant, and Tarsier) of the GenBank databank. We mainly simulate and analyze scaling exponents for three methods in 1D and 2D random walks and Lévy flights. To compare each other between one gene and the other gene in the twelve genomic DNA sequences, the value of the detrended cross-correlation coefficient ρDCCA in {HUMHBB3} and {Dolphin} genes is shown the largest value 0.214 in the 1D Lévy flight with μ=1.5 compared to other sets. The detrended cross-correlation coefficient ρDCCA in {HUMHBB4} and {HUMHBB6} genes is shown the smallest value -0.231 in the 1D Lévy flight with μ=1.5. We particularly find that the combined detrended cross-correlation coefficient ρCDCCA in the two cases of {HUMHBB3}+{Bushbaby} and {HUMHBB6}+{Dolphin} has the largest value 0.331 in the 1D random walk compared to other sets, while that in {HUMHBB3}+{Tarsier} and {HUMHBB4}+{Elephant} has the smallest value -0.295 in the 1D Lévy flight with μ=1.5.

Asymmetric MF-DCCA Method Based on Fluctuation Conduction and its Application in Air Pollution in Hangzhou

The study of the relationship between the concentration of PM2.5 and the local air quality index (AQI) is significant for the improvement of urban air quality. This study not only considered multifractal cross-correlation but also the fluctuation conduction mechanism. An asymmetric multifractal detrended cross-correlation analysis (MF-DCCA) method based on fluctuation conduction is introduced here to empirically explore the causality and conduction time between air quality factors and PM2.5 concentration. The empirical results indicate the existence of a bidirectional fluctuation conduction effect between PM2.5 and PM10, SO2, and NO2 in Hangzhou, China, with a conduction time of 30 hours; this effect is non-existent between PM2.5 and O3. In addition, there is a unidirectional fractal fluctuation conduction between PM2.5 and CO with a conduction time of 21 hours.

Multifractal detrended cross-correlation analysis of the relation between price and volume in European carbon futures markets

Recently, with the development of European carbon futures markets, the complex dynamic relationship between price and volume has become the focus of energy research area. In our research, we applied multifractal detrended cross-correlation analysis method (MF-DCCA) to investigate the cross-correlation and multifractality between price and volume in European carbon futures markets. Firstly, the analysis of basic statistics show that the European carbon futures market has obvious non-normal, peak-heavy-tailed distribution, which does not satisfy the normal distribution hypothesis in the efficient market. Secondly, through multifractal detrended cross-correlation analysis, we conclude that trading price and volume relationship in European carbon futures markets has a non-linear dependence. Thirdly, we find the existence of power-law cross-correlations between price and volume in European carbon futures markets. Finally, the relationship between price and volume is non-linear. After random rearrangement and phase treatment of correlation coefficients, the long-range correlation of small fluctuations and large fluctuations and the fat tail distribution of series are the reasons for the formation of multifractality.

Multifractal detrended partial cross-correlation analysis on Asian markets

The impact of the recession on global markets motivates us to explore the effect of the stock market on the economic stability of a country. The growth of a stock market depends on various factors such as inter-dependency, globalization, economic policies etc. In this paper, we apply the recently developed multifractal detrended partial cross-correlation analysis method on some Asian markets by assuming NASDAQ composite index commonly influences these markets. For this purpose, we analyze the daily returns of three Asian markets such as China’s Shanghai composite index (SHCOMP), Japan’s Nikkei stock average index (NIKKEI 225), and India’s Bombay stock exchange (BSE) sensex index over a period of 18 years (2000–2018). The analysis was performed between all bivariate time series after removing the external influence factor and the results indicate that there exists long-range partial cross-correlation behavior and multifractal nature. For comparison, we also performed analysis without removal of external influence factor i.e. multifractal detrended cross-correlation analysis. From the calculated Hurst scaling exponents, we found that the NASDAQ composite index shows significant influence on these analyzed Asian stock markets.

Multifractal Detrended Cross-Correlation Analysis of Electricity and Carbon Markets in China

With the development of carbon market, the complex dynamic relationship between electricity and carbon market has become the focus of energy research area. In this paper, we applied a new developed multifractal detrended cross-correlation analysis method to investigate the cross-correlation and multifractality between electricity and carbon markets. We analyze the daily return of electricity and carbon prices over a period of 6 years to do the research. The results show that, firstly, we find that there is a strong negative correlation between domestic carbon price and electricity price and a significant cross-correlation between the return series of electricity and carbon markets. Secondly, through multifractal detrended fluctuation analysis, it is proven that there are obvious multifractal characteristics in the return series of electricity and carbon markets, and the results of traditional linear analysis are unreliable. We also find that, based on multifractal detrended cross-correlation analysis, the law cross-correlation between electricity and carbon markets exists significantly. The long-range correlation of small fluctuations and large fluctuations and the fat tail distribution of return series are the reasons for the formation of multifractality.

The effects of exchange rate regime reform on RMB markets: A new perspective based on MF-DCCA

Based on the RMB exchange rate regime reform launched by People’s Bank of China (PBoC) on Aug 11, 2015, we investigate the effects of this reform on the cross-correlations among CNH, NDF and CNY markets using multifractal detrended cross-correlation analysis (MF-DCCA) method. The empirical results show that cross-correlations among CNH, NDF and CNY markets are multifractal. Moreover, we find that this RMB exchange rate reform takes effects, as the persistence and the degree of multifractality of the cross-correlation between these series are weaker after the reform. Furthermore, we find that, after the reform, the persistent cross-correlation for CNH and CNY markets is stronger in the short term but weaker in the long term. While for NDF and CNY markets, the anti-persistent cross-correlation in the short term turn into persistent and persistent cross-correlation in the long term are stronger after the “8.11” RMB exchange rate regime reform. Finally, we find that the fat-tailed distribution is the main source of multifractality for NDF-CNY return series pair after the reform, while for CNH-CNY return series pair, the main source is still long-range correlation.

Neural network and regression methods for optimizations between two meteorological factors

This paper is concerned with the temporal variation characteristics of two meteorological factors (temperature and humidity) in four metropolitan cities (Seoul, Busan, Daegu, Daejeon) in South Korea. Data are extracted from seven years (2008 to 2014) of hourly time series data in meteorological offices of the Korea Meteorological Administration. Using the detrended cross-correlation analysis (DCCA) method, the DCCA coefficient of temperature is compared to that of humidity from daily time series data during four seasons in the four metropolitan cities. In particular, as window size s increases, the DCCA cross-correlation coefficient approaches 0.034 at a time lag of 14 days in the case of spring in Seoul. We find the weak cross-correlation between temperature and humidity at different time lags of 14, 21 and 28 days in spring in Seoul, and the errors ET in the ANN are relatively larger values than that of any other season in both the ANN and the MRM. Particularly, in the ANN model, there exist relatively a large error value of temperature as the characteristics of the non-stationary, deterministically chaotic and noisy meteorological data extracted for the short-term prediction rather than the long-term prediction.

Dynamic relationship between carbon price and coal price: perspective based on Detrended Cross-Correlation Analysis

Unlike the European Union market, the main driver of CO2 price in China seems to be coal price rather than oil and gas prices, considering the coal-dominant energy structure in this country. This study analyzes the interrelationship between coal and carbon prices in both time and time-scale interval perspectives by using the Detrended Cross-Correlation Analysis method. China’s seven pilot carbon markets are taken as the sample. Results show a weak correlation between coal and carbon prices in short time scales (shorter than a quarter) but medium in long time scales (longer than a quarter). For short time scales, the correlation is negative in Chongqing and Shanghai, undetermined in Hubei, and positive for other pilots. For long time scales, Shenzhen pilot remains a negative correlation, Chongqing a positive while others a varying correlation. Results from rolling window method show a dominant weak cross-correlation during the development period of these pilot markets. However, the direction of correlation varies a lot during their developing periods both for the short and the long time scales except for Beijing market. Correlation is enhanced in Shenzhen but weakened in Hubei. In general, there exists a positive cross-correlation in Chinese carbon market and coal market. Such cross-correlation between coal and carbon prices is not usually seen in the European market. China’s coal-dominant energy structure may be the reason behind this phenomenon.

Multifractal analysis of the Chinese stock, bond and fund markets

The stock, bond and fund markets are three important components of a financial market, and the volatility of the markets and correlations between the markets have been paid extensive attention by researchers and investors. In this paper, we devote our efforts to studying the Shanghai financial market, while the return series of Shanghai Composite Index, Shanghai Bond Index and Shanghai Fund Index are considered. Statistical tests are used to detect the nonlinear auto-correlated structures and long-range cross-correlations of the three time series. The multifractal detrended fluctuation analysis and multifractal spectrum analysis methods are applied, by which the existence of multifractality in these three return series are revealed and the sources of multifractality are explored. In particular, the multiscale multifractal detrended cross-correlation analysis method is employed for the first time to generate the Hurst surfaces, which can be used to visualize the dynamic behaviors of cross-correlations among the markets. Empirical results show that the cross-correlations among the markets present different fractal features at different time scales. Further, our study finds that the correlation between the stock and fund markets is stronger than that of the other two groups, and the correlation between the stock and bond markets is unstable. These findings can help to better understand the dynamic mechanisms that govern the volatility of security markets and aid in performing better financial risk assessment and management.

SAD and stock returns revisited: Nonlinear analysis based on MF-DCCA and Granger test

Using both multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DCCA) methods, this paper examines the effects of seasonal affective disorder (SAD) on stock returns in the Chinese stock market. We first separate stocks into small- and large-cap stocks according to market value, and find that SAD and these two stock-return series are multifractal, based on MF-DFA. Moreover, we employ MF-DCCA and show that the cross-correlation between SAD and stock returns is anti-persistent, and the cross-correlation between SAD and return of small-cap stock is stronger than that between SAD and return of large-cap stock. Furthermore, we find that long-range correlation is the main source of multifractality in the cross-correlations between SAD and small-cap stock, while both long-range correlation and fat tails contribute to the multifractality between SAD and large-cap stock. Finally, utilizing nonlinear Granger Causality test, we find that SAD is the Granger cause of stock returns, not vice versa.

Quantifying the cross-correlations between online searches and Bitcoin market

In this paper, we quantify the cross-correlations between Google Trends and Bitcoin market. By employing the Multifractal Detrended Cross-correlation Analysis (MF-DCCA) method, we find that the change of Google Trends (CGT) and Bitcoin market, i.e., returns and changes of volume, are overall significantly cross-correlated based on the cross-correlation test. In particular, the empirical results show that: (1) there exist power-law cross-correlations between CGT and Bitcoin returns as well as CGT and changes of volume; (2) the cross-correlations between CGT and returns have a higher degree of multifractal in the long-term and weak multifractal in the short-term, while the cross-correlations between CGT and change of volume show the opposite trend. (3) with the rolling window analysis, we further find that there is a decrease trend for the cross-correlations between CGT and Bitcoin returns over time, and the cross-correlations scaling exponents are less than 0.5, which indicate that they are both anti-persistent cross-correlated.

Insights from multifractality analysis of tanker freight market volatility with common external factor of crude oil price

This paper is intended for exploring the multifractal features of tanker freight rate market volatility with the common external factor of crude oil price by both the multifractal cross-correlation analysis method (MF-CCA) and the multifractal detrended partial cross-correlation analysis method (MF-DPXA) with consideration of finite size effect. The multifractal spectrums of original, random and surrogate time series are employed to separate the three components of multifractality, and to uncover the influence of financial crisis and oil price on volatility and cross-correlated fluctuations of the tanker freight rates. After the financial crisis in terms of the generalized Hurst exponent, stronger non-linear characteristic and noticeable anti-persistent characteristic in cross correlations between freight rates are supported by the MF-DPXA analysis. Meanwhile, stronger multifractality is indicated by the MF-CCA analysis. These results deepen the understanding of multifractality in tanker freight rate market, and provide investors, shipping related operators or even market players with insight to adjust their marketing strategies.

Multifractal detrended cross-correlation analysis on air pollutants of University of Hyderabad Campus, India

In this paper, we study the multifractal characteristics and cross-correlation behaviour of Air Pollution Index (API) time series data through multifractal detrended cross-correlation analysis method. We analyse the daily API records of nine air pollutants of the university of Hyderabad campus for a period of three years (2013–2016). The cross-correlation behaviour has been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the results, it is found that the cross-correlation analysis shows anti-correlation behaviour for all possible 36 bivariate time series. We also observe the existence of multifractal nature in all the bivariate time series in which many of them show strong multifractal behaviour. In particular, the hazardous particulate matter PM2.5 and inhalable particulate matter PM10 shows anti-correlated behaviour with all air pollutants.

Study on long-range correlation about CODMn in Poyang Lake Inlet and Outlet on the basis of DCCA method

Detrended Cross-Correlation Analysis (DCCA) is applied to the the long-range correlation analysis of CODMn series in Poyang Lake Inlet and Outlet in China. The results show that the two CODMn series are characterized long-range correlation at a certain time scale. Moreover, the correlations between the fluctuations in lake water CODMn do not obey the classical Markov-type stochastic behavior, but display more slowly decaying correlations. This work can be helpful to improvement of modeling of lake water quality.